Predicting Type 1 Diabetes Onset using Novel Survival Analysis with Biomarker Ontology

Type 1 diabetes (T1D) is a chronic autoimmune disease that affects about 1 in 300 children and up to 1 in 100 adults during their life-time1. Improvements in early prediction of T1D onset may help prevent diagnosis for diabetic ketoacidosis, a serious complication often associated with a missed or delayed T1D diagnosis. In addition to genetic factors, progression to T1D is strongly associated with immunologic factors that can be measured during clinical visits. We developed a T1D-specific ontology that captures the dynamic patterns of these biomarkers and used it together with a survival model, RankSvx, proposed in our prior work2. We applied this approach to a T1D dataset harmonized from three birth cohort studies from the United States, Finland, and Sweden. Results show that the dynamic biomarker patterns captured in the proposed ontology are able to improve prediction performance (in concordance index) by 5.3%, 3.3%, 2.8%, and 1.0% over baseline for 3, 6, 9, and 12 month duration windows, respectively.

Liu, BinIBM Research (författare)
Anand, VibhaUniversity of Oulu,IBM Research (författare)
Dunne, Jessica L.Juvenile Diabetes Research Foundation (författare)
Lundgren, MarkusLund University,Lunds universitet,Pediatrisk endokrinologi,Forskargrupper vid Lunds universitet,Paediatric Endocrinology,Lund University Research Groups(Swepub:lu)med-mn2 (författare)
Ng, KenneyIBM Research (författare)
Rewers, MarianUniversity of Colorado (författare)
Veijola, RiittaUniversity of Oulu(Swepub:lu)ri3323ve (författare)
Ghalwash, MohamedIBM Research (författare)
IBM ResearchUniversity of Oulu (creator_code:org_t)
T1DI Study Group

Ingår i:AMIA ... Annual Symposium proceedings. AMIA Symposium2020, s. 727-7361942-597X

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